\received DD MMMM YYYY \acceptedDD MMMM YYYY The rotor of a two-bladed turbine allows the implementation of an additional degree of freedom around the rotating hub y-axis. This idea is over 60 years old, and various research and commercial turbines have had a teeter hinge. Fatigue and extreme loads can be reduced significantly with a teeter hinge. Teetered turbines have been mainly below or around a rotor diameter of 100m. The research project X-Rotor, which has compared two- and three-bladed turbines with a power of 20MW and a diameter of 250m, observed that most teeter movements appear to become less harmful with rising turbine size. Using the analytical equations of the teeter movement combined with numerical simulations of the CART2 (600kW), 2-B Energy’s 2B6 (6MW), and the 2B20HAW-T (20MW, based on the INNWIND.eu turbine), this paper investigates the effects of teeter behavior over a wide range of turbine sizes. It concludes that the Lock number, the ratio of aerodynamic and inertia forces, increases with rotor size, which correlates with the turbine development over the past 50 years and results in a valuable increase in aerodynamic damping.

This study evaluates the performance of a Counter-Rotating Dual Rotor Wind Turbine (CR-DRWT) equipped with a double rotational armature, aimed at enhancing energy capture and efficiency in small-scale wind turbine (SWT) applications. Through wind tunnel testing, the CR-DRWT confirmed earlier findings in literature and achieved a 15 to 50% increase in power output and a 10% increase in efficiency (CP) compared to a single rotor configuration at higher wind speeds (> 7 m/s). Though these gains were not observed at lower wind speeds (4-7m/s). The simplified mechanics of a double rotational armature demonstrated promise for application in SWT’s. The design’s maximum CP values were below those achieved in previous field tests at larger scale highlighting potential for improvement for smaller sized turbines. To further explore the aerodynamics of CR-DRWT’s, computational fluid dynamics (CFD) simulations are recommended, as they could provide insights into optimizing flow dynamics around both rotors. Finally, the study emphasizes the need for precise pitch angle and rotational speed measurements to improve the value of future measurements.

While advancements in the prediction of wind turbine loads and wakes within the actuator line method large eddy simulation (ALM-LES) framework have been an active research topic in recent years, the modelling of the tower and nacelle has received comparatively less attention. This paper investigates different numerical modelling methods for the representation of the support structure and discusses the implications and trade offs associated with each method. Three support structure modelling approaches are investigated: explicitly meshing the support structure; a direct forcing meshless representation; and a combination of actuator disc/line method for the representation of the nacelle/tower. All methods contribute to a more realistic representation of the near wake mean flow when compared to experimental measurements. In the measured far wake, the interaction between the tower and rotor wakes creates asymmetries in the mean flow profiles. The meshed and cell-blocking numerical representations are capable of replicating these far wake asymmetries. In contrast, the actuator method has a less pronounced effect on the far wake mean flow.

This study investigates the capability of FAST.Farm, a mid-fidelity wind farm simulation tool employing the dynamic wake meandering approach, to accurately predict loads on wind turbines in a small wind farm. The wind farm consists of three 1:150 scale models of the DTU 10 MW wind turbine tested in a wind tunnel under scenarios including steady-state operation, wake steering, and dynamic wake actuation. The results demonstrate that FAST.Farm, once calibrated with experimental data, effectively predicts the thrust force and yaw moment of wind turbines across diverse wake conditions. Notably, the Curl wake model—designed to replicate the kidney-shaped wake deficit—has better accuracy in capturing yaw moments of downstream turbines under yaw misalignment. However, its tendency to overestimate wake expansion reduces accuracy in non-skewed inflow scenarios compared to the Polar model. The study highlights the necessity of optimizing FAST.Farm dynamic wake meandering parameters to enhance its precision, particularly by accounting for turbine spacing and wake interactions. Furthermore, it is crucial to improve the accuracy of aerodynamic load calculations under skewed inflow conditions. These findings provide a validated framework for advancing wind farm simulation tools and optimizing wind turbine performance in complex operational conditions.

Conventional methods for manufacturing rotor blades, such as composite construction and die casting, are hindered by high costs due to expensive molds, while 3D printing often results in poor quality or high production costs with unfavorable cost-per-part scaling. Moreover, conventional airfoil designs perform poorly at Reynolds numbers below 100,000, necessitating larger rotors. This becomes especially problematic in wind tunnel studies, where multiple rotors must fit within a single wind tunnel for wake or multirotor research, significantly increasing both building costs and wind tunnel requirements. To address these challenges, this study develops high-performance rotor blades for micro wind turbines that are aerodynamically efficient under low Reynolds number conditions and easy to manufacture. Using cambered plate airfoils, the optimization process employed a class shape transformation and seventh-degree Bernstein polynomials. Aerodynamic performance was analyzed using XFOIL, with evaluations conducted at Reynolds numbers of 30,000, 40,000, and 50,000 to ensure robust performance across realistic operating scenarios. The iterative optimization employed both single-objective and (genetic) multi-objective algorithms, targeting both aerodynamic efficiency and manufacturability. The blade tested with the optimized MB-LR2-7.5 airfoil exhibited superior performance in wind tunnel tests, closely matching Blade Element Momentum (BEM) simulations. This research highlights the potential of cambered plate airfoils to improve micro wind turbine performance while maintaining ease of manufacturing, with potential applications in UAVs, drone propellers, and HVAC systems. The findings advance the understanding of aerodynamic optimization in low Reynolds number environments, paving the way for more efficient and cost-effective rotor designs.

Multibody simulation (MBS) is an established method for modeling wind turbines for purposes such as load calculation. While capable of modeling wind turbines at high levels of detail, large MBS models can be computationally expensive. This makes deployment for real-time critical use cases like digital twins difficult. Machine learning models can be a faster alternative because they only capture relevant details and scale well with parallelization. In this paper, a real-time capable surrogate model is developed with the goal of capturing the operating mode and the tower oscillations of an MBS model. Firstly, several machine learning models based on different architectures are being scaled so that they can be evaluated within the available computational budget. The selected models are being trained on the MBS results. The trained models are being run in a loop with the turbine’s controller and an optional numerical tower oscillator to create the surrogate model. All tested models show good convergence behavior and high R 2 values during training. However, running the models in the surrogate environment proves more difficult as some models diverge from the expected results. The best performing model in the surrogate environment is determined using quantitative criteria.

Quantitation of damping is of great significance for the design and condition assessment of wind turbines. The authors’ previous theoretical and numerical studies showed that compared to damping ratios, a modal aerodynamic damping matrix can better describe the damping coupling in the fore-aft (FA) and side-side (SS) tower motions. In the present study, an improved damping identification method was first proposed to identify this damping matrix with artificial exciters and then verified by using OpenFAST simulations under different excitation frequencies, excitation force amplitudes, and different turbulent wind fields. Following the numerical study, a scaled wind turbine model with a geometric scale ratio of 1/75 was carefully designed based on the NREL 5 MW wind turbine prototype, in which the scaled blade design follows the rule in thrust coefficient similarity. An identification study was performed with this scaled model by a series of wind tunnel tests. The modal aerodynamic damping matrix was identified under steady-state harmonic excitation in the operating state and compared with the identified results by a free decay method and the theoretical values. The results experimentally confirm the correctness of the aerodynamic damping matrix theory under uniform wind and the feasibility of the improved identification method in practice.

The airfoil DU91-W2-150 was investigated in the Low Speed Low Turbulence Tunnel at the Delft University of Technology to study unsteady aerodynamics. This experimental study tested the airfoil under a wide range of angles of attack (AoA) from 0 ◦ to 3 1 0 ◦ at three Reynolds numbers ( Re) from 2 × 1 0 5 to 8 × 1 0 5 . Pressure on the airfoil surface was measured and Particle Image Velocimetry (PIV) measurements were conducted to capture the flow field in the wake. By examining the force coefficient and comparing the wake contours, it shows that an upwind concave surface provides a higher load compared to a convex surface upwind case, highlighting the critical role of surface shape in aerodynamics. When comparing separation at specific locations along the chord for all three Res, it is observed that as Re increases, separation tends to occur at lower angles of attack, both for positive stall and negative stall. The examination of the aerodynamic force variation indicates that, during reverse flow, fluctuations are more pronounced compared to forward flow. This is owing to separation occurring at the aerodynamic leading edge (geometric trailing edge) in reverse flow. In terms of vortex shedding frequency, the study found a nearly constant normalized Strouhal number ( St) of 0.16 across various Res and AoAs in fully separated regions, indicating a consistent pattern under these conditions. However, a slight increase in St, between 0.16 and 0.20, was observed for AoAs exceeding 180 degrees, possibly due to the convex curvature of the airfoil in the upwind direction. In conclusion, this research not only corroborates previous findings for small AoAs, but also adds new data on the aerodynamic behavior of the DU91-W2-150 airfoil under large AoAs, offering various perspectives on the effects of surface curvature, Re, and flow conditions on key aerodynamic parameters.

Due to the nonlinearity, non-stationarity and noise interference of the rolling bearing fault signals of wind turbines, the extraction of features is challenging. This paper proposes a fault diagnosis method for wind turbine bearings based on the improved dung beetle optimizer (IDBO) optimized Long Short-Term Memory (LSTM) network. Firstly, Levy flight strategy and T-distribution perturbation strategy are integrated into the traditional dung beetle optimizer (DBO) to optimize the algorithm. The optimized IDBO algorithm solves the problems of low convergence accuracy and easy entrapment into local optima in the traditional dung beetle algorithm. Secondly, the IDBO algorithm is combined with the long short-term memory method to build the IDBO-LSTM fault diagnosis model. The advantage of fewer parameters in this algorithm enables the model to reduce the overall number of parameters and computational complexity while ensuring the accuracy of temporal feature extraction. Finally, this pa-per takes the bearing open dataset of Case Western Reserve University as an example to compare the proposed method with other models through optimization tests. The experimental results show that the IDBO-LSTM model outperforms other models, with a fault diagnosis accuracy rate of 98.3%. This demonstrates the superiority of the proposed improved dung beetle optimizer and effectively improves the accuracy of fault diagnosis for rolling bearings of wind turbines.

not-yet-known not-yet-known not-yet-known unknown For the current visual detection methods of wind turbine blade defects, their detection models are usually excessively large, making them difficult to achieve a balance between model accuracy and inference speed. To tackle this problem, this paper introduces a lightweight wind turbine blade defect detection network, GCB-YOLO, which attempts to maintain a high detection accuracy and simultaneously achieve a rapid detection speed. At the beginning, a GhostNet network is employed to replace a portion of the YOLOv5s backbone network responsible for feature extraction. This replacement serves to reduce the network’s parameter size and computational load, thereby achieving compression of the feature extraction network. Subsequently, a CA (Channel Attention) mechanism is incorporated into the backbone network, which enhances its ability to focus on small-sized defects. Finally, the neck network PANet is substituted with a Bifpn network, bolstering its ability to discern small-sized defects. A series of validation experiments were conducted using an image dataset gathered from real wind farms. The result showed that the GCB-YOLO exhibited a reduction of 46.2% of model parameter number compared to that of the YOLOv5s. The improved model only has a 7.5MB volume. Hence, in GPU computation mode, the image detection speed reached 115.3 frames per second. More importantly, the proposed method achieved an mAP@0.5 of 94.72%, simplifying the deployment on edge computing devices and simultaneously meeting the real-time defect detection requirement with a sustained high level of detection accuracy.

The monitoring of wind turbine blade root load (BRL) has always been a valuable yet highly challenging problem. BRL monitoring is often measured directly using strain sensors, which is expensive to install and poses safety risks. Thus, it is necessary to explore an indirect measurement method that establishes a mapping relationship between BRL and easily measurable data. This paper presents a non-intrusive BRL monitoring method based on the vibrational signals at bearings (including main bearing, gearbox bearing, and generator bearing) and a new proposed AE-LSTM-Attention neural network. The proposed neural network incorporates autoencoder (AE) pre-training process, long short-term memory (LSTM) neural network, and channel attention mechanism to overcome difficulties in extracting and fusing long-term, high-frequency, and multi-channel signal features. Additionally, considering the wind turbine transmission mechanism, an optimal LSTM step number selection criterion is proposed. The wind turbine’s physical feature is innovatively bridged with the neural network training parameters, e.g., setting the transmission ratio of the gearbox as the number of LSTM steps, by which the most cost-effective prediction performance can be achieved. Experiments were conducted using data from actual operating wind turbines, and the results show that the proposed method can successfully achieve non-intrusive monitoring of BRL characteristics and outperform traditional methods in terms of predictive performance. The mapping relationship between wind turbine vibration signals and BRL characteristics can be established efficiently and accurately.

not-yet-known not-yet-known not-yet-known unknown With increasing wind energy capacity and installation of wind turbines, new inspection techniques are being explored to examine wind turbine rotor blades, especially during operation. A common result of surface damage phenomena (such as leading-edge erosion) is the premature transition of laminar to turbulent flow on the surface of rotor blades. In the KI-VISIR (Künstliche Intelligenz Visuell und Infrarot Thermografie – Artificial Intelligence-Visual and Infrared Thermography) project, infrared thermography is used as an inspection tool to capture so-called thermal turbulence patterns (TTP) that result from such surface contamination or damage. To compliment the thermographic inspections, high-resolution photography is performed to visualise, in detail, the sites where these turbulence patterns initiate. A convolutional neural network (CNN) was developed and used to detect and localise the turbulence patterns. A unique dataset combining the thermograms and visual images of operational wind turbine rotor blades has been provided, along with the simplified annotations for the turbulence patterns. Additional tools are available to allow users to use the data requiring only basic Python programming skills.

A coupled medium-fidelity drivetrain model is developed and implemented in OpenFAST for a 10-MW land-based reference turbine. The implementation is verified against a fully coupled multibody wind turbine model, including a detailed drivetrain. The new model can simultaneously and accurately estimate main bearing loads and represent elastic bending of the drivetrain. It has low computational cost, useful for early design phases, sensitivity analyses and complex systems like wind farms (where computational expense must be expended elsewhere). Here, the model is extended to a monopile offshore wind turbine and used to investigate sensitivity of predicted main bearing rolling contact fatigue to different synthetic turbulence models. Large eddy simulations (LES) intentionally targeting stable, neutral, and unstable atmospheric conditions at below-, near- and above-rated wind speeds, were used as a reference. The turbulence models recommended by IEC, the Mann spectral tensor model and the Kaimal spectral model with exponential coherence, were fitted to the LES data. Additionally, a constrained turbulence generator, PyConTurb, based on LES data, was applied in the aero-hydro-servo-elastic simulations. Taking PyConTurb as the baseline, the Kaimal model significantly underestimates fatigue of the downwind main bearing, with between 10 and 40% less damage. The Mann model also underestimates the downwind main bearing fatigue with up to 30%. The upwind main bearing damage is driven by mean loads, and differences between models are less significant, although the trends are similar. Reasons for these discrepancies are investigated and attributed to differences in spatial and temporal variations among the turbulence models.

Wind turbines operating in unsteady and complex environments often encounter asymmetric and unsteady vortex flows on the leeward side of the blades, leading to airflow separation and dynamic stall. These issues significantly impact the aerodynamic performance and energy efficiency of wind turbines. However, there is a lack of research regarding the regulatory relationship between airfoil parameters, trailing-edge flap oscillation angles, and aerodynamic parameters. To address this research gap, this study employs a multi-block overlapping grid technique and a custom-programmed method to simulate active flow control on wind turbine blades by coordinating the motion of the main wing and trailing-edge flap during pitching oscillations. Aerodynamic parameters of airfoils were computed to investigate the impact of trailing-edge flap deflection direction, deflection amplitude, and other parameters on the wind turbine blade thrust, torque, and aerodynamic characteristics under deep stall conditions. An optimized aerodynamic performance curve of the blade was produced. The results show that when a deep stall occurred and the wind speed was low, the lift coefficient of the airfoil can be increased by approximately 23% with the amplitude αamp1=40° and the same deflection direction; moreover, the tangential force and torque value on the blade concurrently increased. Adjusting the motion of the main wing and trailing-edge flap reduced the thrust and load fluctuation of the blade by changing the flap amplitude value. Additionally, the operation efficiency of the wind turbine improved. These results can provide a reference for the active flow control and application of wind turbine flaps.

A versatile and widely used tool in atmospheric research and operational forecasting is the Weather Research and Forecasting (WRF) model. The WRF model utilizes advanced algorithms and physics parameterizations to simulate complicated atmospheric phenomena. However, this model relies on a configuration that encompasses spin-up time, different Planetary Boundary Layer (PBL), and Land Surface Model (LSM) schemes to describe the physics near the surface. This work aims to optimize the configuration of the WRF model by evaluating the impact of the spin-up period, PBL, and LSM schemes to obtain the best representation of the wind field to calculate the power production of a wind farm located in the complex terrain of La Rumorosa, Baja California, Mexico. This site was chosen because of the availability of data, the challenging conditions of the terrain complexity, and severe changes in the wind direction. Results indicate that one day spin-up is the best option to predict the wind speed for one month or even one year. Moreover, the best statistical metrics are obtained when the Mellor-Yamada-Nakanishi–Niino Level 2.5 (MYNN2) PBL scheme is coupled with Noah-Multiphysics LSM. The best set-up for the WRF model was used to simulate the performance of a wind turbine in the farm with excellent results.

Floating offshore wind (FOW) is a renewable energy source that is set to play an essential role in addressing climate change and the need for sustainable development. However, due to the increasing threat of climate emergency, more wind turbines are required to be deployed in deep water locations, further offshore. This presents heightened challenges for accessing the turbines and performing maintenance, leading to increased costs. Naturally, methods to reduce operational expenditure (OpEx) are highly desirable. One method that shows potential for reducing OpEx of FOW is LIDAR-assisted pitch control. This approach uses wind velocity measurements from a nacelle-mounted LIDAR to enable feedforward control of floating offshore wind turbines (FOWTs), and can result in reductions to the variations of structural loads. Results obtained from a previous study of combined feedforward collective and individual pitch control (FFCPC+FFIPC) are translated to OpEx reductions via reduced component failure rates for future FOW developments, namely in locations awarded in the recent ScotWind leasing round. The results indicate that LIDAR-assisted pitch control may allow for an up to 5% reduction in OpEx, increasing up to 11% with workability constraints included. The results varied across the three ScotWind sites considered, with sites furthest from shore reaping the greatest benefit from LIDAR-assisted control. This work highlights the potential savings and reduction in the overall levelised cost of energy for future offshore wind turbine projects deliverable through the implementation of LIDAR-assisted pitch control.

Real-time, proportional deployment of an Active Gurney Flap (AGF) located near the trailing edge of the wind turbine blade on the pressure surface is used to modulate aerodynamic performance. Wind tunnel experiments are conducted using an AGF model based on the geometry of a commonly used wind turbine blade over a range of Re numbers from 160 ,000 to 414 ,000, angle of attack (AOA) from - 1 0 ◦ to 1 5 ◦ , and AGF deployment positions from 0 ◦ to 1 3 5 ◦ . The torque required to raise and lower the AGF was measured and found to be approximately C T = 0 . 1 , independent of the deployment rate and the Re number. Aerodynamic force measurements demonstrate that the deployment of the AGF is capable of improving the lift and pitch moment performance with an increased lift-to-drag ratio. Aerodynamic changes are proportional to the AGF deployment angle, with a maximum effect observed at 7 0 ◦ to the chord. The aerodynamic changes occur instantly as the flap is deployed, although sudden changes in aerodynamic loading induce mechanical vibration of the wind tunnel model on the supporting sting.

Wind energy is one of the main renewable energy sources in the current energy transition. Due to ever more and ever larger wind turbines (WT), the requirements for WT operation become more complex. Model predictive control (MPC) for WTs shows the potential to handle these requirements and conflicting control objectives in a single optimization-based controller. Recent research has widely investigated MPC for WT in simulation, but mostly lacks experimental validation. This work aims to experimentally validate MPC on a full-scale WT under real conditions. To this end, we combine an Extendend Kalman Filter for nonlinear state estimation with robust linear time-varying MPC. We evaluate the proposed control algorithm in terms of time-domain performance and power curve in simulation. However, the main contribution of this work is the experimental validation on a 3MW WT in Northern Germany with a total duration of 3h continuous full access of the controller. We were able to demonstrate stable operation of the proposed MPC in the upper partial load regime, transition regime and lower full load regime, at measured wind speeds between 4.76m/s and 13.06m/s, inside and outside the wake shadow of another WT. The power curve determined in simulation shows comparable results to a reference feedback controller. The MPC formulation combines several control objectives in a single optimization problem, yet the tuning effort still remains complex. In future work, we plan to reduce the complexity of the control loop based on this experimentally validated MPC. We provide our experimental data at https://github.com/rwth-irt/MPC_WT_experiment.